A High-Precision Short-Term Photovoltaic Power Forecasting Model Based on Multivariate Variational Mode Decomposition and Gated Recurrent Unit-Attention with Crested Porcupine Optimizer-Enhanced Vector Weighted Average Algorithm [PDF]
SensorsThe increasing reliance on renewable energy sources, such as photovoltaic (PV) systems, is pivotal for achieving sustainable development and addressing global energy challenges. However, short-term power forecasting for distributed PV systems often faces
Jinxiang Pian, Xianliang Chen
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A Field Verification Denoising Method for Partial Discharge Ultrasonic Sensors Based on IPSO-Optimated Multivariate Variational Mode Decomposition Combined with Improved Wavelet Transforms [PDF]
SensorsField verification of contact-type ultrasonic sensors enables rapid evaluation of their sensitivity performance, thereby ensuring the accuracy of partial discharge (PD) ultrasonic monitoring results.
Tienan Cao +8 more
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Energies, 2023 In response to the volatility of photovoltaic power generation, this paper proposes a short-term photovoltaic power generation prediction model (HWOA-MVMD-TPA-TCN) based on a Hybrid Whale Optimization Algorithm (HWOA), multivariate variational mode ...
Ranran Cao +4 more
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Forecasting of interval carbon price in China based on decomposition-reconstruction-ensemble framework [PDF]
Carbon Balance and ManagementAccurate prediction of carbon prices is imperative for the effective management of carbon markets and the facilitation of a global transition to green energy.
Beibei Hu, Yunhe Cheng
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Towards intelligent air quality forecasting using integrated machine learning framework with variational mode decomposition and catboost feature selection [PDF]
Scientific ReportsPredicting air pollution is crucial in improving air quality (AQ), which consequently provides benefits to the ecosystems and human health. AQ predictions often make use of Machine Learning (ML) approaches; nevertheless, these methods are not without ...
Iman Ahmadianfar +10 more
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Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition [PDF]
Front Neurosci, 2020 Several approaches can be used to estimate neural activity. The main differences between them concern the a priori information used and its sensitivity to high noise levels.
Bueno-Lopez, Maximiliano +4 more
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Deep Learning Method Based on Multivariate Variational Mode Decomposition for Classification of Epileptic Signals [PDF]
Brain SciencesBackground/Objectives: Epilepsy is a neurological disorder that severely impacts patients’ quality of life. In clinical practice, specific pharmacological and surgical interventions are tailored to distinct seizure types.
Shang Zhang +3 more
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Short-Term Wind Power Prediction Based on MVMD-AVOA-CNN-LSTM-AM
International Transactions on Electrical Energy SystemsDue to the intermittent and fluctuating nature of wind power generation, it is difficult to achieve the desired prediction accuracy for wind power prediction.
Xiqing Zang +3 more
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Multiscale Functional Connectivity analysis of episodic memory reconstruction [PDF]
Frontiers in CognitionOur ability to share memories constitutes a social foundation of our world. When exposed to another person's memory, individuals can mentally reconstruct the events described, even if they were not present during the related events.
Manuel Morante +4 more
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Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition
Remote SensingNoise suppression is essential in time-domain electromagnetic (TDEM) data processing and interpretation. TDEM data are typically in broadband signal, which makes it difficult to separate the signal in the whole frequency band.
Kang Xing +3 more
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